Show/Hide Menu
Hide/Show Apps
Logout
Türkçe
Türkçe
Search
Search
Login
Login
OpenMETU
OpenMETU
About
About
Open Science Policy
Open Science Policy
Open Access Guideline
Open Access Guideline
Postgraduate Thesis Guideline
Postgraduate Thesis Guideline
Communities & Collections
Communities & Collections
Help
Help
Frequently Asked Questions
Frequently Asked Questions
Guides
Guides
Thesis submission
Thesis submission
MS without thesis term project submission
MS without thesis term project submission
Publication submission with DOI
Publication submission with DOI
Publication submission
Publication submission
Supporting Information
Supporting Information
General Information
General Information
Copyright, Embargo and License
Copyright, Embargo and License
Contact us
Contact us
Heterogeneous Sensor Data Fusion for Target Classification Using Adaptive Distance Function
Date
2021-04-01
Author
Atıcı, Bengü
Karasakal, Esra
Karasakal, Orhan
Metadata
Show full item record
This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
.
Item Usage Stats
324
views
0
downloads
Cite This
Automatic Target Recognition (ATR) systems are used as decisionsupport systems to classify the potential targets in military applications. Thesesystems are composed of four phases, which are selection of sensors, preprocessingof radar data, feature extraction and selection, and processing of features toclassify potential targets. In this study, the classification phase of an ATR systemhaving heterogeneous sensors is considered. We propose novel multiple criteriaclassification methods based on the modified Dempster–Shafer theory. Ensembleof classifiers is used as the first step probabilistic classification algorithm. Artificialneural network and support vector machine are employed in the ensemble. Eachnon-imaginary dataset coming from heterogeneous sensors is classified by bothclassifiers in the ensemble, and the classification result that has a higher accuracyratio is chosen for each of the sensors. The proposed data fusion algorithms areused to combine the sensors’ results to reach the final class of the target.We presentextensive computational results that show the merits of the proposed algorithms.
URI
https://hdl.handle.net/11511/90354
Relation
Multiple Criteria Decision Making: Beyond the Information Age
Collections
Department of Industrial Engineering, Book / Book chapter
Suggestions
OpenMETU
Core
Multi-Aspect Data Fusion Applied to Electromagnetic Target Classification using Enetic Algorithm
Sayan, Gönül (Kluwer Academic Publishers, 2002-01-01)
Electromagnetic target detection and classification is an important problem relevant not only to military applications but also to civilian use. In the problem of a breast tumor detection and identification [1], for instance, the main concern is accuracy. In the case of the recognition of a military target such as an aircraft or a ship, on the other hand, speed of classification is as important as the accuracy of the decision as such a decision should be made within a fraction of a second. For...
Multi-aspect data fusion applied to electromagnetic target classification using enetic algorithm
Sayan, Gönül (2000-07-07)
Electromagnetic target detection and classification is an important problem relevant not only to military applications but also to civilian use. In the problem of a breast tumor detection and identification [1], for instance, the main concern is accuracy. In the case of the recognition of a military target such as an aircraft or a ship, on the other hand, speed of classification is as important as the accuracy of the decision as such a decision should be made within a fraction of a second. For the K-pulse t...
Parametric estimation of clutter autocorrelation matrix for ground moving target indication
Kalender, Emre; Tanık, Yalçın; Department of Electrical and Electronics Engineering (2013)
In airborne radar systems with Ground Moving Target Indication (GMTI) mode, it is desired to detect the presence of targets in the interference consisting of noise, ground clutter, and jamming signals. These interference components usually mask the target return signal, such that the detection requires suppression of the interference signals. Space-time adaptive processing is a widely used interference suppression technique which uses temporal and spatial information to eliminate the effects of clutter and ...
SWIR objective design using seidel aberration theory
Aslan, Serhat Hasan; Yerli, Sinan Kaan; Keskin, Onur; Department of Physics (2016)
Optical systems are used for increasing the situational awareness and Intelligence, Surveillance and Reconnaissance (ISR) capabilities for military purposes. MWIR (Midwave infrared) and LWIR (Long wave infrared) waveband informations are the first two wavebands information in the atmospheric transmission window that are harnessed in military night vision optical systems. Another candidate of these operable wavebands is the SWIR (Shortwave infrared). Shortwave infrared (SWIR) imaging is an extension of Near ...
Efficient analysis of large array antennas
Ovalı, Fatih; Aydın Çivi, Hatice Özlem; Department of Electrical and Electronics Engineering (2004)
Large phased array antennas are widely used in many military and commercial applications. The analysis of large arrays containing many antenna or frequency-selective (FSS) surface elements is inefficient or intractable when brute force numerical methods are used. For the efficient analysis of such structures hybrid methods (analytic and numerical, numerical and numerical) can be used. In this thesis, a hybrid method combining the uniform geometrical theory of diffraction (UTD) and the moment method (MoM) us...
Citation Formats
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
B. Atıcı, E. Karasakal, and O. Karasakal,
Heterogeneous Sensor Data Fusion for Target Classification Using Adaptive Distance Function
. 2021.